Agentic AI in Auditing: Navigating the Next Frontier

Introduction

The auditing profession is undergoing a significant transformation with the emergence of agentic AI—autonomous systems capable of making decisions and executing tasks with minimal human intervention. Unlike traditional AI, which operates based on predefined rules and human prompts, agentic AI possesses the ability to plan, adapt, and act independently to achieve specified objectives. This evolution presents both unprecedented opportunities and complex challenges for auditors, regulators, and organizations alike.

As agentic AI systems become more integrated into auditing processes, they promise enhanced efficiency, real-time risk assessment, and the ability to handle vast datasets beyond human capacity. However, their autonomous nature raises critical questions about accountability, transparency, and ethical governance. Understanding the implications of agentic AI is essential for stakeholders aiming to harness its benefits while mitigating associated risks.

This article explores how agentic AI is being adopted by leading firms, particularly the Big Four; the benefits and efficiency gains it offers; and the unique governance, ethical, and regulatory concerns it raises. It also examines the evolving role of human auditors as oversight professionals and ethical stewards in an environment increasingly managed by intelligent agents. For foundational background on AI’s prior influence in audit, see AI Audit & Assurance Transformation.

What is Agentic AI?

Agentic AI is a transformative branch of artificial intelligence that refers to systems capable of autonomous goal-setting, problem-solving, and action execution with minimal or no human prompting. While traditional AI systems require constant oversight and predefined instructions, agentic AI possesses what many consider the next evolutionary leap in automation: the ability to reason, plan, act, and adapt dynamically to changing environments in pursuit of a defined outcome.

According to NVIDIA, agentic AI operates with a degree of agency, similar to human decision-making. It combines perception, language understanding, knowledge retrieval, and iterative planning into a loop that can modify strategies based on feedback. These agents are not static tools; they are interactive systems capable of making decisions in real time, adjusting goals based on risk and opportunity, and executing multi-step operations with contextual awareness.

In practical terms, agentic AI represents a shift from AI as a tool to AI as a collaborator or even a co-pilot. In contrast to traditional robotic process automation (RPA), which handles rule-based repetitive tasks, agentic systems can evaluate audit anomalies, identify outliers in massive datasets, and trigger investigative procedures without waiting for manual cues. This opens up new possibilities for audit efficiency and scope, especially in high-volume transaction environments like global supply chains or financial institutions.

The Institute of Internal Auditors emphasizes that the potential of agentic AI lies in its ability to elevate audit practices from static assessments to continuous, intelligent oversight. For example, an audit agent could independently monitor enterprise resource planning (ERP) systems to detect compliance violations, assess the severity of issues, and escalate cases that meet predefined thresholds. These systems also learn over time, refining their anomaly detection logic and improving precision.

However, not all agentic systems are created equal. The level of autonomy can vary, from semi-agentic platforms that require periodic human checkpoints to fully autonomous systems operating 24/7 without supervision. While the latter is still in pilot stages, the trajectory suggests increasing adoption as trust in these systems matures and governance frameworks evolve to support their deployment.

In the auditing profession, where trust, transparency, and repeatability are foundational, the adoption of agentic AI represents both an opportunity and a challenge. It holds promise for scaling internal audit capacity, reducing cycle times, and proactively managing emerging risks. However, it also necessitates a rethinking of control systems, documentation standards, and ethical oversight. As the technology advances, auditors must become fluent in how agentic systems function—not only to leverage them effectively but to audit them meaningfully as well.

Benefits and Use Cases in Audit

Agentic AI is revolutionizing the auditing landscape by automating complex tasks, enhancing accuracy, and enabling real-time decision-making. Its integration into audit processes offers numerous benefits and practical applications that are reshaping the profession.

Enhanced Efficiency and Productivity

By automating routine audit tasks, agentic AI allows auditors to focus on strategic activities. According to Akira.ai, agentic AI can automate up to 80% of routine audit tasks, leading to a 30% boost in productivity. This efficiency not only reduces manual effort but also accelerates audit cycles.

Real-Time Risk Assessment and Monitoring

Agentic AI enables continuous monitoring of financial transactions and controls, facilitating real-time risk assessment. As highlighted by AuditBoard, AI agents can proactively identify anomalies and compliance issues, allowing for immediate corrective actions and reducing the risk of financial misstatements.

Improved Accuracy and Consistency

The precision of agentic AI minimizes human errors and ensures consistent application of audit procedures. This leads to more reliable audit outcomes and enhances stakeholder confidence in financial reporting.

Scalability and Adaptability

Agentic AI systems can easily scale to accommodate growing data volumes and adapt to changing regulatory requirements. This scalability ensures that audit functions remain effective and compliant in dynamic business environments.

Use Case: Accelerated Audit Processes

A notable example is AES, a global energy company, which implemented agentic AI to streamline its energy safety audits. This adoption resulted in a 99% reduction in audit costs and a time reduction from 14 days to one hour, with an increase of 10-20% in accuracy, as reported by Converge Technology Solutions.

Use Case: Continuous Compliance Monitoring

Agentic AI facilitates ongoing compliance monitoring by autonomously reviewing financial records and identifying potential issues. This continuous oversight helps organizations maintain adherence to regulatory standards and promptly address any deviations.

Use Case: Strategic Decision Support

Beyond operational tasks, agentic AI provides strategic insights by analyzing complex data sets to inform decision-making. This capability supports auditors in identifying trends, forecasting risks, and advising on business strategies.

Risks and Ethical Concerns

While agentic AI offers transformative potential in auditing, it also introduces significant risks and ethical challenges that organizations must address proactively.

Algorithmic Bias and Fairness

Agentic AI systems can inadvertently perpetuate existing biases present in training data, leading to unfair or discriminatory outcomes. For instance, biased data can result in certain transactions being unjustly flagged as high-risk. Mitigating this requires the use of diverse datasets and continuous monitoring to detect and correct biases, as highlighted by Lucinity.

Lack of Transparency and Explainability

The decision-making processes of agentic AI are often opaque, making it challenging for auditors to understand and trust the outcomes. Implementing explainable AI (XAI) methodologies is crucial to ensure transparency and maintain stakeholder confidence, as discussed by RPATech.

Accountability and Legal Liability

Determining responsibility for decisions made by autonomous AI agents is complex. In cases of errors or unintended consequences, it's unclear who should be held accountable—the developers, the deploying organization, or the AI system itself. Establishing clear governance structures and accountability frameworks is essential, as emphasized by NAVEX.

Security Vulnerabilities

Agentic AI systems can introduce new security risks, such as unauthorized access or manipulation. Ensuring robust cybersecurity measures and regular audits can help mitigate these vulnerabilities, as outlined by the Global Skill Development Council.

Regulatory Compliance Challenges

The rapid advancement of agentic AI outpaces existing regulatory frameworks, creating compliance challenges. Organizations must stay informed about evolving regulations and adapt their practices accordingly. Implementing risk scoring and differentiated approval workflows can aid in aligning AI deployments with compliance requirements, as suggested by YourDataConnect.

Governance and Regulatory Implications

The integration of agentic AI into auditing processes necessitates a robust governance framework to ensure compliance with evolving regulatory standards. As these autonomous systems gain prominence, organizations must address the unique challenges they present.

Establishing Proactive Governance Models

Agentic AI systems require governance structures that balance autonomy with accountability. According to BigID, implementing self-regulating models allows AI agents to adhere to ethical and legal constraints while maintaining human oversight. This approach ensures that AI actions align with organizational values and regulatory requirements.

Compliance Challenges and Risk Management

Deploying agentic AI introduces compliance complexities, particularly concerning data privacy and decision-making transparency. As highlighted by NAVEX, organizations must perform rigorous risk assessments and establish controls to mitigate potential legal and ethical risks associated with AI autonomy.

Regulatory Frameworks and Standards

The European Union's AI Act exemplifies the move towards structured AI regulation, categorizing AI systems based on risk levels. However, as noted by HiddenLayer, agentic AI's dynamic nature may not fit neatly into existing categories, necessitating adaptive regulatory approaches that account for their evolving behaviors.

Enhancing Compliance through Automation

Agentic AI can also bolster compliance efforts by automating regulatory monitoring and policy updates. Regology discusses how AI agents can translate regulatory changes into actionable tasks, ensuring timely adherence to new requirements and reducing the risk of non-compliance.

Future Outlook and Strategic Recommendations

The emergence of agentic AI is not merely another wave of automation—it represents a structural transformation of how audits are conceptualized, executed, and interpreted. Looking ahead, agentic AI will evolve from tactical augmentation tools into strategic actors capable of influencing core governance practices. The auditing profession is on the brink of an evolution that will redefine its value proposition within the enterprise.

Anticipated Developments in Agentic AI

Current applications of agentic AI are focused on enhancing transactional analysis and procedural audits. However, the next phase will likely include agent networks capable of multi-agent coordination and distributed decision-making. According to ISG, we should expect integration of agentic AI into full-cycle risk management processes—where agents initiate control testing, simulate risk scenarios, and autonomously recommend risk mitigation plans.

Additionally, Microsoft notes that enterprises are beginning to embed agentic AI within AI-first business strategies. This means AI agents will not just assist audits, but lead proactive assurance roles—identifying emerging regulatory changes, flagging probable non-compliance, and preparing draft audit narratives based on evolving benchmarks. These changes will require a fundamental rethinking of audit lifecycles and standards.

Agentic systems are also expected to integrate with ESG assurance, cybersecurity audits, and cross-border compliance monitoring—enabling firms to conduct thematic audits in near real time. This capability will become indispensable in multi-jurisdictional enterprises operating under varying regulatory regimes.

Strategic Recommendations for Organizations

To unlock these opportunities, audit leaders must act now. The following strategic initiatives are critical to ensuring agentic AI is deployed securely, ethically, and effectively:

• 1. Build Organizational AI Fluency: Audit professionals must understand AI concepts beyond surface-level automation. Internal upskilling programs, technical bootcamps, and AI ethics workshops should be integrated into standard training curricula.
• 2. Define Agent Boundaries and Objectives: According to Productive Edge, organizations must clearly define the goals, access controls, and decision-making limits of each AI agent before deployment. This ensures agents operate within an aligned governance structure.
• 3. Create AI-Audit Collaboration Models: New models of audit execution will emerge where agents and auditors operate in tandem. For example, agents might compile anomaly reports, while auditors verify significance and context. Role clarity is critical to avoid redundant or conflicting assessments.
• 4. Standardize Explainability Protocols: As per RSM, real-time explainability is essential for trust. Auditing teams must establish standards that require agents to document rationale, data lineage, and confidence levels behind each action.
• 5. Participate in AI Regulation Design: The pace of regulation will lag behind innovation. Enterprises should actively collaborate with regulatory bodies and industry consortia to shape policies around agentic AI, especially concerning transparency, accountability, and cross-border audit equivalency.
• 6. Pilot Use Cases Before Enterprise Rollout: Begin with narrow implementations—such as autonomous walkthrough testing or document validation—before expanding to full audit scopes. Controlled pilots reduce disruption while building institutional learning.

Longer term, audit leadership must institutionalize agile operating models to adapt to rapidly advancing AI capabilities. Governance, risk, and compliance (GRC) platforms should be redesigned to natively accommodate agentic workflows—featuring audit trails for AI reasoning, real-time risk scoring, and integration hooks for intelligent dashboards.

With appropriate strategic preparation, agentic AI can transform audits from backward-looking compliance checks into continuous, strategic functions that proactively manage risk and build enterprise resilience.

Conclusion

The integration of agentic AI into auditing represents a transformative shift in how organizations approach risk management, compliance, and operational efficiency. Unlike traditional AI systems, agentic AI possesses the autonomy to make decisions, adapt to new information, and execute complex tasks without constant human oversight.

As highlighted by AuditBoard, the adoption of AI agents in internal audit functions enables continuous monitoring and real-time risk assessment, enhancing the agility and responsiveness of audit processes. Moreover, the Institute of Internal Auditors emphasizes the evolving role of auditors, who must now develop skills to work alongside intelligent systems, interpreting AI-driven insights and making informed judgments.

However, the deployment of agentic AI is not without challenges. Organizations must address concerns related to transparency, accountability, and ethical considerations. Establishing robust governance frameworks and ensuring compliance with regulatory standards are essential steps in mitigating potential risks associated with autonomous decision-making systems.

Looking ahead, the successful integration of agentic AI into auditing will depend on a balanced approach that combines technological innovation with human expertise. As noted by MobiDev, businesses that embrace this synergy are poised to achieve greater efficiency, accuracy, and strategic insight in their audit functions.